





'''simple docstring'''



from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block






@dataclass
class 				__lowercase							(   snake_case__    ):







	'''simple docstring'''

	a     :					Any           =							42






class 				__lowercase							(   snake_case__			,   snake_case__    ):







	'''simple docstring'''

	@register_to_config
	def __init__(self  ,_lowerCamelCase = 65536  ,_lowerCamelCase = None  ,_lowerCamelCase = 2  ,_lowerCamelCase = 2  ,_lowerCamelCase = 0  ,_lowerCamelCase = "fourier"  ,_lowerCamelCase = True  ,_lowerCamelCase = False  ,_lowerCamelCase = 0.0  ,_lowerCamelCase = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")  ,_lowerCamelCase = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")  ,_lowerCamelCase = "UNetMidBlock1D"  ,_lowerCamelCase = None  ,_lowerCamelCase = (32, 32, 64)  ,_lowerCamelCase = None  ,_lowerCamelCase = 8  ,_lowerCamelCase = 1  ,_lowerCamelCase = False  ,)				->				Tuple:







				'''simple docstring'''





				super().__init__()
				__lowercase					    =				sample_size

				# time
				if time_embedding_type == "fourier":
							__lowercase					    =				GaussianFourierProjection(
							    embedding_size=8  ,set_W_to_weight=__snake_case  ,log=__snake_case  ,flip_sin_to_cos=__snake_case			)
							__lowercase					    =				2 * block_out_channels[0]
				elif time_embedding_type == "positional":
							__lowercase					    =				Timesteps(
							    block_out_channels[0]  ,flip_sin_to_cos=__snake_case  ,downscale_freq_shift=__snake_case			)
							__lowercase					    =				block_out_channels[0]

				if use_timestep_embedding:
							__lowercase					    =				block_out_channels[0] * 4
							__lowercase					    =				TimestepEmbedding(
							    in_channels=__snake_case  ,time_embed_dim=__snake_case  ,act_fn=__snake_case  ,out_dim=block_out_channels[0]  ,)

				__lowercase					    =				nn.ModuleList([]			)
				__lowercase					    =				None
				__lowercase					    =				nn.ModuleList([]			)
				__lowercase					    =				None

				# down
				__lowercase					    =				in_channels
				for i, down_block_type in enumerate(__snake_case			):
							__lowercase					    =				output_channel
							__lowercase					    =				block_out_channels[i]

							if i == 0:
										input_channel += extra_in_channels

							__lowercase					    =				i == len(__snake_case			) - 1

							__lowercase					    =				get_down_block(
							    __snake_case  ,num_layers=__snake_case  ,in_channels=__snake_case  ,out_channels=__snake_case  ,temb_channels=block_out_channels[0]  ,add_downsample=not is_final_block or downsample_each_block  ,)
							self.down_blocks.append(__snake_case			)

				# mid
				__lowercase					    =				get_mid_block(
				    __snake_case  ,in_channels=block_out_channels[-1]  ,mid_channels=block_out_channels[-1]  ,out_channels=block_out_channels[-1]  ,embed_dim=block_out_channels[0]  ,num_layers=__snake_case  ,add_downsample=__snake_case  ,)

				# up
				__lowercase					    =				list(reversed(__snake_case			)			)
				__lowercase					    =				reversed_block_out_channels[0]
				if out_block_type is None:
							__lowercase					    =				out_channels
				else:
							__lowercase					    =				block_out_channels[0]

				for i, up_block_type in enumerate(__snake_case			):
							__lowercase					    =				output_channel
							__lowercase					    =				(
							    reversed_block_out_channels[i + 1] if i < len(__snake_case			) - 1 else final_upsample_channels
							)

							__lowercase					    =				i == len(__snake_case			) - 1

							__lowercase					    =				get_up_block(
							    __snake_case  ,num_layers=__snake_case  ,in_channels=__snake_case  ,out_channels=__snake_case  ,temb_channels=block_out_channels[0]  ,add_upsample=not is_final_block  ,)
							self.up_blocks.append(__snake_case			)
							__lowercase					    =				output_channel

				# out
				__lowercase					    =				norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4  ,32			)
				__lowercase					    =				get_out_block(
				    out_block_type=__snake_case  ,num_groups_out=__snake_case  ,embed_dim=block_out_channels[0]  ,out_channels=__snake_case  ,act_fn=__snake_case  ,fc_dim=block_out_channels[-1] // 4  ,)


	def     _UpperCAmelCase  (self  ,_lowerCamelCase  ,_lowerCamelCase  ,_lowerCamelCase = True  ,)				->				Union[str, Any]:







				'''simple docstring'''





				__lowercase					    =				timestep
				if not torch.is_tensor(__snake_case			):
							__lowercase					    =				torch.tensor([timesteps]  ,dtype=torch.long  ,device=sample.device			)
				elif torch.is_tensor(__snake_case			) and len(timesteps.shape			) == 0:
							__lowercase					    =				timesteps[None].to(sample.device			)

				__lowercase					    =				self.time_proj(__snake_case			)
				if self.config.use_timestep_embedding:
							__lowercase					    =				self.time_mlp(__snake_case			)
				else:
							__lowercase					    =				timestep_embed[..., None]
							__lowercase					    =				timestep_embed.repeat([1, 1, sample.shape[2]]			).to(sample.dtype			)
							__lowercase					    =				timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])			)

				# 2. down
				__lowercase					    =				()
				for downsample_block in self.down_blocks:
							__lowercase    ,		__lowercase					    =				downsample_block(hidden_states=__snake_case  ,temb=__snake_case			)
							down_block_res_samples += res_samples

				# 3. mid
				if self.mid_block:
							__lowercase					    =				self.mid_block(__snake_case  ,__snake_case			)

				# 4. up
				for i, upsample_block in enumerate(self.up_blocks			):
							__lowercase					    =				down_block_res_samples[-1:]
							__lowercase					    =				down_block_res_samples[:-1]
							__lowercase					    =				upsample_block(__snake_case  ,res_hidden_states_tuple=__snake_case  ,temb=__snake_case			)

				# 5. post-process
				if self.out_block:
							__lowercase					    =				self.out_block(__snake_case  ,__snake_case			)

				if not return_dict:
							return (sample,)

				return UNetaDOutput(sample=__snake_case			)









import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor

@require_vision
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def 						a_     (			self       ):
         snake_case											=							tempfile.mkdtemp()

         snake_case											=							[
             '''[UNK]''',
             '''[CLS]''',
             '''[SEP]''',
             '''[PAD]''',
             '''[MASK]''',
             '''的''',
             '''价''',
             '''格''',
             '''是''',
             '''15''',
             '''便''',
             '''alex''',
             '''##andra''',
             '''，''',
             '''。''',
             '''-''',
             '''t''',
             '''shirt''',
         ]
         snake_case											=							os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES['''vocab_file''']       )
         with open(self.vocab_file     ,   '''w'''     ,   encoding='''utf-8'''       ) as vocab_writer:
              vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]       )       )

         snake_case											=							{
             '''do_resize''': True,
             '''size''': {'''height''': 2_2_4, '''width''': 2_2_4},
             '''do_center_crop''': True,
             '''crop_size''': {'''height''': 1_8, '''width''': 1_8},
             '''do_normalize''': True,
             '''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
             '''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
             '''do_convert_rgb''': True,
         }
         snake_case											=							os.path.join(self.tmpdirname     ,   __snake_case       )
         with open(self.image_processor_file     ,   '''w'''     ,   encoding='''utf-8'''       ) as fp:
              json.dump(__snake_case     ,   __snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return BertTokenizer.from_pretrained(self.tmpdirname     ,   **__snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return BertTokenizerFast.from_pretrained(self.tmpdirname     ,   **__snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname     ,   **__snake_case       )
    def 						a_     (			self       ):
         shutil.rmtree(self.tmpdirname       )
    def 						a_     (			self       ):
         snake_case											=							[np.random.randint(2_5_5     ,   size=(3, 3_0, 4_0_0)     ,   dtype=np.uinta       )]

         snake_case											=							[Image.fromarray(np.moveaxis(__snake_case     ,   0     ,   -1       )       ) for x in image_inputs]

         return image_inputs
    def 						a_     (			self       ):
         snake_case											=							self.get_tokenizer()
         snake_case											=							self.get_rust_tokenizer()
         snake_case											=							self.get_image_processor()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )
         processor_slow.save_pretrained(self.tmpdirname       )
         snake_case											=							ChineseCLIPProcessor.from_pretrained(self.tmpdirname     ,   use_fast=__snake_case       )

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )
         processor_fast.save_pretrained(self.tmpdirname       )
         snake_case											=							ChineseCLIPProcessor.from_pretrained(self.tmpdirname       )

         self.assertEqual(processor_slow.tokenizer.get_vocab()     ,   tokenizer_slow.get_vocab()       )
         self.assertEqual(processor_fast.tokenizer.get_vocab()     ,   tokenizer_fast.get_vocab()       )
         self.assertEqual(tokenizer_slow.get_vocab()     ,   tokenizer_fast.get_vocab()       )
         self.assertIsInstance(processor_slow.tokenizer     ,   __snake_case       )
         self.assertIsInstance(processor_fast.tokenizer     ,   __snake_case       )

         self.assertEqual(processor_slow.image_processor.to_json_string()     ,   image_processor.to_json_string()       )
         self.assertEqual(processor_fast.image_processor.to_json_string()     ,   image_processor.to_json_string()       )
         self.assertIsInstance(processor_slow.image_processor     ,   __snake_case       )
         self.assertIsInstance(processor_fast.image_processor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							ChineseCLIPProcessor(tokenizer=self.get_tokenizer()     ,   image_processor=self.get_image_processor()       )
         processor.save_pretrained(self.tmpdirname       )

         snake_case											=							self.get_tokenizer(cls_token='''(CLS)'''     ,   sep_token='''(SEP)'''       )
         snake_case											=							self.get_image_processor(do_normalize=__snake_case       )

         snake_case											=							ChineseCLIPProcessor.from_pretrained(
             self.tmpdirname     ,   cls_token='''(CLS)'''     ,   sep_token='''(SEP)'''     ,   do_normalize=__snake_case       )

         self.assertEqual(processor.tokenizer.get_vocab()     ,   tokenizer_add_kwargs.get_vocab()       )
         self.assertIsInstance(processor.tokenizer     ,   __snake_case       )

         self.assertEqual(processor.image_processor.to_json_string()     ,   image_processor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.image_processor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )

         snake_case											=							self.prepare_image_inputs()

         snake_case											=							image_processor(__snake_case     ,   return_tensors='''np'''       )
         snake_case											=							processor(images=__snake_case     ,   return_tensors='''np'''       )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )

         snake_case											=							'''Alexandra，T-shirt的价格是15便士。'''

         snake_case											=							processor(text=__snake_case       )

         snake_case											=							tokenizer(__snake_case       )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]     ,   encoded_processor[key]       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )

         snake_case											=							'''Alexandra，T-shirt的价格是15便士。'''
         snake_case											=							self.prepare_image_inputs()

         snake_case											=							processor(text=__snake_case     ,   images=__snake_case       )

         self.assertListEqual(list(inputs.keys()       )     ,   ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']       )

         # test if it raises when no input is passed
         with pytest.raises(__snake_case       ):
              processor()
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )

         snake_case											=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         snake_case											=							processor.batch_decode(__snake_case       )
         snake_case											=							tokenizer.batch_decode(__snake_case       )

         self.assertListEqual(__snake_case     ,   __snake_case       )




    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )

         snake_case											=							'''Alexandra，T-shirt的价格是15便士。'''
         snake_case											=							self.prepare_image_inputs()

         snake_case											=							processor(text=__snake_case     ,   images=__snake_case       )

         self.assertListEqual(list(inputs.keys()       )     ,   processor.model_input_names       )




